Pages that link to "Item:Q2022488"
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The following pages link to A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C (Q2022488):
Displaying 11 items.
- Mathematical optimization in classification and regression trees (Q828748) (← links)
- An ASP-based approach to counterfactual explanations for classification (Q1995447) (← links)
- Counterfactual state explanations for reinforcement learning agents via generative deep learning (Q2238641) (← links)
- Counterfactual reasoning and learning systems: the example of computational advertising (Q2933945) (← links)
- Counterfactual Explanation of Machine Learning Survival Models (Q5862149) (← links)
- On optimal regression trees to detect critical intervals for multivariate functional data (Q6164347) (← links)
- Considerations when learning additive explanations for black-box models (Q6176233) (← links)
- A Symbolic Approach for Counterfactual Explanations (Q6486027) (← links)
- Explainable AI for operational research: a defining framework, methods, applications, and a research agenda (Q6572853) (← links)
- A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data (Q6572858) (← links)
- A new model for counterfactual analysis for functional data (Q6661125) (← links)
